Deep neural networks (DNNs) have made significant progress, but often suffer from fairness
issues, as deep models typically show distinct accuracy differences among certain …

Abstract Fairness issues in Deep Learning models have recently received increasing
attention due to their significant societal impact. Although methods for mitigating unfairness …

Deep Neural Networks (DNNs) have achieved tremendous success in many applications,
while it has been demonstrated that DNNs can exhibit some undesirable behaviors on …

The out-of-distribution (OOD) problem generally arises when neural networks encounter
data that significantly deviates from the training data distribution, ie, in-distribution (InD). In …

Deep neural network (DNN) testing approaches have grown fast in recent years to test the
correctness and robustness of DNNs. In particular, DNN coverage criteria are frequently …

Quality assurance of deep neural networks (DNNs) is crucial for the deployment of DNN-
based software, especially in mission-and safety-critical tasks. Inspired by structural white …

Abstract As Generative Artificial Intelligence (GenAI) technologies evolve at an
unprecedented rate, global governance approaches struggle to keep pace with the …

Crowdsourcing Federated learning (CFL) is a new crowdsourcing development paradigm
for the Deep Neural Network (DNN) models, also called “software 2.0”. In practice, the …

Deep neural networks (DNNs) have demonstrated their outperformance in various software
systems, but also exhibit misbehavior and even result in irreversible disasters. Therefore, it …

Abstract Context Erroneous decisions of Deep Neural Networks may pose a significant
threat to Deep Learning systems deployed in security-critical domains. The key to testing …